A blog of Bridge Environment, updated weekly on Thursdays, travel permitting.
Bridge Environment seeks to catalyze a cultural shift in how our society addresses environmental issues. We provide relevant and unbiased advice to any interested party, and also work to educate scientists, policy makers, and the public on how to have a more informative dialog over environmental issues.

Last week, I defined, put into historical context, and considered the human health effects
of genetically modified (GM) food items, also known as GMOs.
While promising to explore environmental and political/economic considerations
in future blogs, I concluded that human health concerns were more modest than
critics would have you believe, but that there is potential value in conducting
long-term testing of food varieties, GM or otherwise, that differ in
substantial ways from their predecessors. As I will describe in the
political/economic post, there may also be reasons to worry about financial and
political influences on the approval process.

About the same time as that blog posting, a scientific study
was published on the health of pigs which ate a GM diet (Carman et al. 2013). The authors used what one might
call a shotgun approach. Instead of a targeted study with a specific concern in
mind, they examined many anatomical and biochemical characteristics of 84
slaughtered pigs fed a GMO diet and 84 fed an equivalent non-GMO diet. Out of
the many characteristics they tested, they found differences they described as
statistically significant for two characteristics: uterine size and the rate of
severe stomach inflammation. Though there have been a few balanced blog posts
and media reports about this study, the majority used headlines such as “GMO
feed turns pig stomachs to mush!,” perhaps not surprising for a source called
Natural News. But MSN Now ran “GMO feed wreaked havoc on pigs’ stomachs.” If
general media reports are to be believed, this study confirmed our fears: GMO
foods do horrible things to our health.

Is it true? Was my advice last week off-base? Let me start by
reassuring you that the study does not change my conclusions at all. Here is
why.

First, let’s consider uteruses. According to the authors,
they were 25% larger in pigs fed GMO corn and soy (median 0.105% of total body
weight versus 0.086% body weight) and this difference was stastically different
at the 2.5% level (known as a p-value, where p stands for probability). However,
their claim is complicated and, in some cases misleading, due to several factors:

the math: as reported in their tables, uteruses of GMO-fed
pigs were 22% larger than those of non-GMO-fed pigs, but this may be a typo
since the results from the table do not match what is reported in the text;

attrition: several pigs died in the experiment (11 non-GM-fed
pigs and 12 GM-fed pigs) and one non-GM-fed female pig failed to develop a
uterus at all, so there may be a bias based on which pigs developed and
survived until the end of the experiment; and

the health significance: we have no understanding of whether
larger uteruses for pigs at this stage in development is a good or bad thing;
in fact GMO-fed pigs were slightly larger at slaughter so the difference may
simply indicate faster sexual maturity.

The authors’ claim of statistical significance raises even
more concerns. Scientists typically only make strong claims about results if
observed differences have a 5% or smaller chance of occurring due to random
variation. Scientists picked the 5% p-value threshold because of a desire to
maintain high standards prior to claiming that an observed difference is real. Even
then, one in twenty times a scientist will report a meaningful finding that was
simply due to random differences among similar individuals.

This grey area of scientific proof becomes far murkier when
multiple comparisons are made. Because of the shotgun appraoch of this study,
where one treatment was conducted and many comparisons were made, we would
expect a far greater chance of an observed difference being due to chance than
if only one observation had been made. My first real statistics professor
referred to such shotgun approaches as p-ing all over the page, and this paper
is guilty. For example, they measured eight separate organs from the same set
of pigs. In each of those eight comparisons, there would be a 5% chance of
mistakenly thinking there was an effect of GMO feed when in fact the difference
was random chance. Collectively over the eight comparisons, there would be a 1
in 3 chance1 of thinking at least one organ size difference was
attributable to diet when in fact the pigs were essentially the same. To
correct for this multiple comparison bias, scientists are supposed to adjust
the threshold for considering a result significant. In the case of eight
comparisons, the new standard of significance would be 0.64% for each organ,
and the authors’ p-value of 2.5% would not be adequate to claim a true
difference between the uteruses of GMO- and non-GMO-fed pigs.

Inflamed stomachs were even more problematic than large
uteruses. The authors claim that severe inflammation occurred over 2.5 times
more often in GM-fed pigs and that the difference at a p-value of 0.4%.
However, the authors made 16 separate comparisons of pathological conditions.
To correct for the multiple comparisons, they should have adjusted their
significance level per condition down to 0.32%. Once again, valid use of
statistics would keep them from claiming a true difference. It is even more
interesting when we examine the other observed differences between GM-fed and
non-GM-fed pigs, many of which were related to the stomach. Whereas GM-fed pigs
more often had severe stomach inflammation, they also more commonly had no
inflammation, and less often had mild or moderate inflammation. There are
statistical tests to compare multiple category data like these, but it is not
surprising that the authors failed to use them considering their failure to
address multiple comparisons. Had they performed it, such an analysis would
have provided ambivalent results because of the fact that GM-fed pigs had
higher incidence of stomach health but also of severe inflammation. GM-fed pigs
also had lower incidence of stomach erosion, pin-point ulcers, and bleeding
ulcers, but higher incidence of frank ulcers (not sure what they are…aren’t all
ulcers honest?). GM-fed pigs also had lower incidence of heart, liver, and
spleen abnormalitlies. Mind you, none of these differences were statistically
significant, either, so all of this analysis should be taken with a very large
grain of salt.

What really stands out for me in this study is not the effect
of a GM diet, but the condition of all pigs raised commercially for meat
production. Over the course of these pigs’ short lifetime (less than six
months), more than one in eight died prior to slaughter, even with veterinary
treatment. The article reassures us that these death rates are “within expected
rates for US commercial piggeries.” Of the survivors, more than 1 in 10 had
heart abnormalities, 1 in 5 had abnormal lymph nodes, over half had moderate to
severe stomach inflammation, nearly 3 in 5 had pneumonia, and 4 in 5 had
stomach erosions. The condition of these animals definitely makes me ponder eating more seafood.

Back to GMO health effects…applying scientific standards for
statistical interpretation, this study becomes inconclusive. We could choose to
be like the authors and interpret trends in the data that may simply be a
result of random chance. This exercise yields a complex picture without any
obvious indication that GM-fed pigs were healthier or less healthy than their
non-GMO-fed counterparts. Should we dismiss the findings entirely? I don’t
think so. Some of those trends may be a result of real effects. However, follow
up study would be necessary and should be focused on particular concerns and
analyzed correctly. At this point, though, there still is no credible evidence
of health effects associated with common GMO food supplies. I maintain my
conclusions from last week, and promise to flesh out larger concerns
surrounding environmental impacts and political/economic influence over the
coming weeks.

News outlets that presented this research otherwise have
shown you their lack of respect for understanding science and, purposely or
inadvertently, played on our human tendency to panic over uncertainties. You
might want to consider better news sources in the future.

1 If
each comparison is treated as significant when the statistics report a 5%
chance of mistaking random variation for a true result, then each has a 95%
chance of correctly identifying random differences as being just that. To get
it correct for eight different comparisons, we have to multiply 0.95 by itself
eight times, 0.958 = 0.66. In other words, the likelihood that we
correctly eight observed differences as due to random chance is only 66%,
leaving a 34% chance…one in three, of seeing at least one false positive.

Friday, June 14, 2013

This
entry is the first in a three-part series about genetically modified organisms
(GMOs)

GMOs: Monsters or unsung superheroes?

Genetically modified (GM) food items prompt one of two
reactions. Some people panic, not wanting anything to do with them. The panic reaction
has dominated European politics and led to a near-ban on growing (but not
importing) GMOs. Other people ignore the issue, preferring to remain untroubled
by yet another risk of modern life. The denial reaction has dominated US
politics so far, where even efforts to label GMOs have fallen flat. These two
reactions should not be surprising: they
are our natural human responses to uncertainty. To foster a more
rational debate regarding GM foods, we present a three-part series. Today we
define the term GM, put GM practices into historical context, and consider
human health effects. Next week, we will discuss potential ecological
consequences. In two weeks, we will wrap up the series with a discussion of
intellectual property, market power, and politics. In general, we will conclude
that GMOs pose greater risks than proponents would have you believe, but
smaller ones than critics suggest. Rather than ban them outright, we encourage
smart regulation along with accurate education. With proper oversight and regulation,
GMOs are neither terrible monsters nor superheroes capable of saving us from ourselves.

To
start, let’s define GMOs by putting them into historical context. Humans have shaped
our food supply throughout history and have, through farming practices, caused purposeful
genetic modifications in nearly everything we eat. In large part, these modifications
are a result of selective breeding1, whereby people produce the next
generation of domesticated plants or animals using prize specimens. Over
millennia, selective breeding practices shaped the genes of domesticated
grains, vegetables, fruits, meats, and even microbial products like cheese and
wine, making them more productive and more desirable to consume. In this way,
nearly everything we eat could be considered genetically-modified. However, the
GMO term is reserved for a specific method of creating new varieties.

The
next big technology for shaping our food supply came in the first half of the 20th
century when agricultural scientists began causing mutations in plants by
exposing them to radiation or harsh chemicals. The resulting individuals are
screened for desirable traits and, in some cases, interbred with existing strains
to further improve them. The varieties resulting from these processes are even
more genetically-modified than those developed via selective breeding alone,
but still do not qualify for the term GMOs.

Instead
of relying on mutagenic conditions, true GMOs are developed in a calculated and
precise way. Specific properties are sought, appropriate genes are identified
in other organisms, and then a GMO is engineered by combining the genes of
multiple plants or animals, a process referred to as transgenesis. This process
is inspired by transduction. Discovered in the early 1950s, transduction is a natural
phenomenon by which certain viruses are capable of incorporating a piece of DNA
from one host into their own genome, carrying it to another host, and inserting
it into the new host’s DNA. As creepy as this phenomenon sounds, it can be beneficial.
Transduction is involved in the rapid evolution of antibiotic resistance in
bacteria, for example (good for the bacteria even if it is not for us), and has
promise for inserting functional gene copies into cells of people who suffer
from genetic disorders. When creating a GMO, scientists transfer DNA using
plasmids, which have many similarities with the transduction-capable genetic
material of viruses. The scientists’ goal is to create a transgenic
superorganism.

In
some ways, the creation of GMOs is merely a more controlled version of techniques
we have used for millennia. From a policy perspective, what separates this
technique is the rapidity and scope of changes that can be made. The rate of
change offers both promise and peril. For example, consider AquaAdvantage salmon,
also known as the Frankenfish. This GM salmon has been engineered by adding
genes from Chinook salmon (Oncorhynchus
tshawytscha) and ocean pout (Zoarces
americanus) to Atlantic salmon (Salmo
salar). The introduced genes allow the engineered salmon to grow twice as
fast as existing varieties of farmed Atlantic salmon.

The
transgenic nature of GMOs tends to fuel our imagination and make us believe we
are eating something contaminated. In reality, GMOs are made with precise and
controlled technologies compared to the older radiation- and chemical-based
methods. Regardless, the genetic changes are the one major lasting effect of
the environment that created new varieties using either of these techniques. If
there are health risks, they are most likely going to be from the resulting
properties of the food, not the details of its creation.

When
it comes to health consequences, GM foods are not particularly different from other
varieties humans have developed throughout our history. It is possible that any
new variant may have unintentional health effects. The obvious solution to this
challenge is testing, which does take place on GMOs prior to human consumption.
Testing is capable of identifying major toxic issues quickly, but not as
capable of identifying rarer problems like an unusual allergy, or long-term risk
for diseases like cancer, which may only manifest in some individuals and only after
years of exposure.

GMOs
have been in our food supply since the mid-1990s and no human health issues
have yet been identified. This result does not mean that GMOs are all safe for
human consumption. The fact that they can differ so quickly and dramatically
from previous varieties means that GMOs should be subject to additional
scrutiny and longer-term testing than, for example, a variety derived from
selective breeding. However, we should not see GMOs as somehow wholly distinct.
In all cases where a new variety is notably novel, we should consider more
extensive and longer-term testing.

The
same logic applies to labeling. While labeling seems reasonable from a
perspective of informed consumers, producers are legitimately worried that a
GMO label would be seen by the public as a hazard warning. Such a warning may
be warranted if the public does not have faith in the ability of food
regulators to accurately gauge the health risks associated with novel food
items. However, such a concern could be applied equally well to food items
derived via radiation- or chemical-exposure. If we do label, we should do so
based on the novelty of a food and accompany that effort with a public
education campaign.

This
does not necessarily mean that GMOs are safe, but it does mean that health concerns
are less of a factor in regulating GMOs than environmental risks and the
influences of monopoly power. They will be the subject of our blog entries in
the following two weeks.

Thursday, June 6, 2013

Last
month, I posted a couple of blog entries about the US economy. One highlighted
that fiscal stimulus, while most
likely a good idea, requires a balanced perspective based on risk management.
Another contrasted the typical effectiveness of
economists
with the ineffectiveness of ecologists at influencing policy, despite similar
information gaps and system complexities. Whereas I feel economists do
generally succeed and perform well in informing many policy decisions, fiscal
stimulus is not the only exception. There has also been a general failure to
inform what economists would describe as distributional consequences or, more
simply put, fairness.

The
current projections of US debt serve as an excellent example where economic
analysis on equity is available but generally has not entered into public
debate. Much of the facts in the following paragraph come from an
information-intensive but ideologically-conservative source, JustFacts. I prefer to
avoid ideology, but do appreciate well-researched arguments on either side of
an issue.

Our
official government debt presently stands at $16.7 trillion. However, this
figure does not account for outstanding obligations. If the government were a
publicly-traded company, it would be required to include:

Using
these values and taking into account assets that the government holds (cash,
loan holdings, inventories, etc.), the government has a shortfall of $67.7
trillion dollars.

This
figure is still not the full story. The government is projected to run ongoing
budget deficits, which will add to the debt. On the other hand, the government
has created public infrastructure which is worth a substantial amount of money.
Neither of these issues is accounted for in the $67.7 trillion dollar figure.

This
debt figure is eye-catching and worthy of consideration in its own right, but
becomes even more fascinating when we consider its distributional consequences.
Let’s consider the winners and losers of three different sources of deficit:
excess past spending, Social Security, and Medicare.

Past
spending (including commitments to programs like the federal retirement system)
makes up about a third of the total deficit figure. Heavy debt is, for the most
part, a recent phenomenon. Earlier governments generally ran modest deficits
and contributed greatly to the country’s infrastructure and potential for
economic growth. Here are a few examples. The Civilian Conservation Corps
engineering projects in the 1930s created water supplies and economic
opportunities in many parts of the country. The G.I. Bill provided college
education to millions of World War II veterans, and a sophisticated work force
for the country. The interstate highway system, which began in the 1950s,
allowed for efficient movement of people and goods across the country. This
sort of project is important to keep in mind when weighing debt because of the
long-term value it creates, much like the fact that a family investment in a
new business is very different from buying big screen TV.

Our
current debt came primarily during the 1980s, 1990s, and past 10 years, when
tax cuts were not balanced by spending reductions. This debt did not provide
major infrastructure improvements. Instead it funded ongoing discretionary
government spending along with weapons development (e.g., the Cold War) and military
campaigns (e.g., Iraq, Afghanistan, Iraq again). The benefits from earlier
generations’ investments as part of relatively balanced budgets continue to pay
off to society at large. The deficit spending of the 1980s, 1990s, and present
supported our current economy but may not add much future value. Who are the
winners from this debt? The wealthy surely benefited, not only from tax breaks
but also from the fact that they have garnered the bulk of recent economic
growth. The poor may have broken even: they have seen job opportunities and some
key services (e.g., public universities) shrink. On the other hand, they too
have received tax breaks/credits, and the lost job opportunities may be more
related to cheap unskilled overseas labor alternatives than to U.S. fiscal
policy. Future generations definitely lose out since we most likely will pass
on this debt without the same level of infrastructure investment of previous
generations.

Social
Security and Medicare make up the rest of the deficit. These two programs are
paid for through payroll taxes and provide benefits once people reach
retirement age. Social Security began in 1935, during the Great Depression. The
idea of a government health insurance program was debated for decades before
Medicare was enacted in 1965, and the end product was scaled back to cover only
retirees. Neither program functions as a savings account. Money for current
outlays comes from taxes levied on current employees. However, the tax rates
for these programs are roughly designed so that an individual’s contributions while
working will pay for their expected expenses in retirement. However, the rate
calculations are not dynamic, meaning they do not change terribly often. The
rates for both programs have been the same since the late 1980s/early 1990s.
More importantly, the rates reflect survival rates of retirees at the time. In
reality, there have been dramatic increases in life expectancy for Americans
since Social Security was enacted. A sizable portion of this increase comes
from reduced infant mortality, which does not affect these programs since
people who die before they begin to work do not contribute to nor benefit from
them. However, life expectancy at age 20 has also steadily improved and
continues to do so. In 1940, slightly more than half of 20 year olds lived to
reach 65, and those who did typically lived an extra 12 years. Today, about 80
percent of 20 year olds reach 65 and typically live an extra 18 years. As a
result, people spend more years in retirement but contribute over the same
number of years worked. For Social Security, and to a lesser extent Medicare,
we run a bit of a Ponzi scheme. The first
entrants had no history of paying taxes to fund these entitlements and so got
them at the expense of workers at the time. Because we continue to
underestimate longevity while calculating the tax rates, retirees still get
more than they paid for at the expense of current workers.

As
with any Ponzi scheme, these programs will work well until they finally don’t. Baby
Boomers just might be the breaking point. They are no different from retirees
that came before them in having underfunded their own public retirement and
health insurance. What makes them different is their sheer numbers. Birth rates
fell through the early 1900s as a result of industrialization, then spiked
following World War II. Whereas the relatively large numbers of babies in the
early 20th century were offset by high mortality rates, working-age Baby
Boomers are surviving longer than any generation before them. The result is
that, whereas large numbers of Boomers contributed funds their retired elders,
the ratios are shifting and fewer workers will be supporting more retirees.
Given the Ponzi scheme nature of Social Security and Medicare, we will see an extreme
burden on the post-Baby Boomers, partly because tax rates were insufficient to
bankroll enough savings to support the Baby Boomers in retirement, and partly
because the burden of making up the gap will fall on relatively few working adults.
Who wins from the resulting deficits? First generation recipients of Social
Security and Medicare were the biggest winners since they had not paid into the
systems. Generations following them received lesser benefits but still got
some. As with any Ponzi scheme, it is the final investors who take the loss.
Here we have a choice. We can scale back the scope of these programs by, for
example, raising the retirement age or reducing benefits. Doing so would spread
the pain out over multiple generations. Or, we can go on our current
trajectory, which will leave a much smaller group of Americans to bear the
brunt of the costs.

This
issue is not just one of equity. As the debt figures quoted earlier indicate,
addressing Social Security and Medicare will get us a long way towards tackling
the federal deficit. Doing so would reduce the chances of broader economic
stagnation and give the government more flexibility to use tools like stimulus
funds to smooth out bumps in our economic performance.